




"""simple docstring"""




import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


lowerCAmelCase				:     Optional[Any]					=				NewType("""DataClass""", Any)
lowerCAmelCase				:     int					=				NewType("""DataClassType""", Any)



def        a__					(							snake_case__							)		->   Optional[int]:
			if isinstance(snake_case__			,					snake_case__							):
						return v
			if v.lower() in ("yes", "true", "t", "y", "1"):
						return True
			elif v.lower() in ("no", "false", "f", "n", "0"):
						return False
			else:
						raise ArgumentTypeError(
						    F'Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).'							)



def        a__					(							snake_case__							)		->   Callable[[str], Any]:
			lowerCamelCase      =   {str(snake_case__							): choice for choice in choices}
			return lambda snake_case__							: str_to_choice.get(snake_case__			,					snake_case__							)



def        a__					(							*,
    snake_case__ = None			,					snake_case__ = None			,					snake_case__ = dataclasses.MISSING			,					snake_case__ = dataclasses.MISSING			,					snake_case__ = None			,					**snake_case__			,					)		->   dataclasses.Field:
			if metadata is None:
						# Important, don't use as default param in function signature because dict is mutable and shared across function calls
						lowerCamelCase      =   {}
			if aliases is not None:
						lowerCamelCase      =   aliases
			if help is not None:
						lowerCamelCase      =   help

			return dataclasses.field(metadata=snake_case__			,					default=snake_case__			,					default_factory=snake_case__			,					**snake_case__							)






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		42





				def __init__(			self						,					_a						,					**_a					):





							"""simple docstring"""
							# To make the default appear when using --help
							if "formatter_class" not in kwargs:
										lowerCamelCase      =   ArgumentDefaultsHelpFormatter
							super().__init__(**_a					)
							if dataclasses.is_dataclass(_a					):
										lowerCamelCase      =   [dataclass_types]
							lowerCamelCase      =   list(_a					)
							for dtype in self.dataclass_types:
										self._add_dataclass_arguments(_a					)





				@staticmethod
				def   _lowerCAmelCase							(			_a						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   f'--{field.name}'
							lowerCamelCase      =   field.metadata.copy()
							# field.metadata is not used at all by Data Classes,
							# it is provided as a third-party extension mechanism.
							if isinstance(field.type						,					_a					):
										raise RuntimeError(
										    """Unresolved type detected, which should have been done with the help of """
										    """`typing.get_type_hints` method by default"""					)

							lowerCamelCase      =   kwargs.pop("""aliases"""						,					[]					)
							if isinstance(_a						,					_a					):
										lowerCamelCase      =   [aliases]

							lowerCamelCase      =   getattr(field.type						,					"""__origin__"""						,					field.type					)
							if origin_type is Union or (hasattr(_a						,					"""UnionType"""					) and isinstance(_a						,					types.UnionType					)):
										if str not in field.type.__args__ and (
										    len(field.type.__args__					) != 2 or type(_a					) not in field.type.__args__
										):
													raise ValueError(
													    """Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"""
													    """ the argument parser only supports one type per argument."""
													    f' Problem encountered in field \'{field.name}\'.'					)
										if type(_a					) not in field.type.__args__:
													# filter `str` in Union
													lowerCamelCase      =   field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
													lowerCamelCase      =   getattr(field.type						,					"""__origin__"""						,					field.type					)
										elif bool not in field.type.__args__:
													# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
													lowerCamelCase      =   (
													    field.type.__args__[0] if isinstance(_a						,					field.type.__args__[1]					) else field.type.__args__[1]
													)
													lowerCamelCase      =   getattr(field.type						,					"""__origin__"""						,					field.type					)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
							lowerCamelCase      =   {}
							if origin_type is Literal or (isinstance(field.type						,					_a					) and issubclass(field.type						,					_a					)):
										if origin_type is Literal:
													lowerCamelCase      =   field.type.__args__
										else:
													lowerCamelCase      =   [x.value for x in field.type]

										lowerCamelCase      =   make_choice_type_function(kwargs["""choices"""]					)

										if field.default is not dataclasses.MISSING:
													lowerCamelCase      =   field.default
										else:
													lowerCamelCase      =   True
							elif field.type is bool or field.type == Optional[bool]:
										# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
										# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
										lowerCamelCase      =   copy(_a					)

										# Hack because type=bool in argparse does not behave as we want.
										lowerCamelCase      =   string_to_bool
										if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
													# Default value is False if we have no default when of type bool.
													lowerCamelCase      =   False if field.default is dataclasses.MISSING else field.default
													# This is the value that will get picked if we don't include --field_name in any way
													lowerCamelCase      =   default
													# This tells argparse we accept 0 or 1 value after --field_name
													lowerCamelCase      =   """?"""
													# This is the value that will get picked if we do --field_name (without value)
													lowerCamelCase      =   True
							elif isclass(_a					) and issubclass(_a						,					_a					):
										lowerCamelCase      =   field.type.__args__[0]
										lowerCamelCase      =   """+"""
										if field.default_factory is not dataclasses.MISSING:
													lowerCamelCase      =   field.default_factory()
										elif field.default is dataclasses.MISSING:
													lowerCamelCase      =   True
							else:
										lowerCamelCase      =   field.type
										if field.default is not dataclasses.MISSING:
													lowerCamelCase      =   field.default
										elif field.default_factory is not dataclasses.MISSING:
													lowerCamelCase      =   field.default_factory()
										else:
													lowerCamelCase      =   True
							parser.add_argument(_a						,					*_a						,					**_a					)

							# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
							# Order is important for arguments with the same destination!
							# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
							# here and we do not need those changes/additional keys.
							if field.default is True and (field.type is bool or field.type == Optional[bool]):
										lowerCamelCase      =   False
										parser.add_argument(f'--no_{field.name}'						,					action="""store_false"""						,					dest=field.name						,					**_a					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if hasattr(_a						,					"""_argument_group_name"""					):
										lowerCamelCase      =   self.add_argument_group(dtype._argument_group_name					)
							else:
										lowerCamelCase      =   self

							try:
										lowerCamelCase      =   get_type_hints(_a					)
							except NameError:
										raise RuntimeError(
										    f'Type resolution failed for {dtype}. Try declaring the class in global scope or '
										    """removing line of `from __future__ import annotations` which opts in Postponed """
										    """Evaluation of Annotations (PEP 563)"""					)
							except TypeError as ex:
										# Remove this block when we drop Python 3.9 support
										if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(_a					):
													lowerCamelCase      =   """.""".join(map(_a						,					sys.version_info[:3]					)					)
													raise RuntimeError(
													    f'Type resolution failed for {dtype} on Python {python_version}. Try removing '
													    """line of `from __future__ import annotations` which opts in union types as """
													    """`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To """
													    """support Python versions that lower than 3.10, you need to use """
													    """`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of """
													    """`X | None`."""					) from ex
										raise

							for field in dataclasses.fields(_a					):
										if not field.init:
													continue
										lowerCamelCase      =   type_hints[field.name]
										self._parse_dataclass_field(_a						,					_a					)





				def   _lowerCAmelCase							(			self						,					_a=None						,					_a=False						,					_a=True						,					_a=None						,					_a=None						,					):





							"""simple docstring"""

							if args_file_flag or args_filename or (look_for_args_file and len(sys.argv					)):
										lowerCamelCase      =   []

										if args_filename:
													args_files.append(Path(_a					)					)
										elif look_for_args_file and len(sys.argv					):
													args_files.append(Path(sys.argv[0]					).with_suffix(""".args"""					)					)

										# args files specified via command line flag should overwrite default args files so we add them last
										if args_file_flag:
													# Create special parser just to extract the args_file_flag values
													lowerCamelCase      =   ArgumentParser()
													args_file_parser.add_argument(_a						,					type=_a						,					action="""append"""					)

													# Use only remaining args for further parsing (remove the args_file_flag)
													lowerCamelCase ,  lowerCamelCase      =   args_file_parser.parse_known_args(args=_a					)
													lowerCamelCase      =   vars(_a					).get(args_file_flag.lstrip("""-"""					)						,					_a					)

													if cmd_args_file_paths:
																args_files.extend([Path(_a					) for p in cmd_args_file_paths]					)

										lowerCamelCase      =   []
										for args_file in args_files:
													if args_file.exists():
																file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
										lowerCamelCase      =   file_args + args if args is not None else file_args + sys.argv[1:]
							lowerCamelCase ,  lowerCamelCase      =   self.parse_known_args(args=_a					)
							lowerCamelCase      =   []
							for dtype in self.dataclass_types:
										lowerCamelCase      =   {f.name for f in dataclasses.fields(_a					) if f.init}
										lowerCamelCase      =   {k: v for k, v in vars(_a					).items() if k in keys}
										for k in keys:
													delattr(_a						,					_a					)
										lowerCamelCase      =   dtype(**_a					)
										outputs.append(_a					)
							if len(namespace.__dict__					) > 0:
										# additional namespace.
										outputs.append(_a					)
							if return_remaining_strings:
										return (*outputs, remaining_args)
							else:
										if remaining_args:
													raise ValueError(f'Some specified arguments are not used by the HfArgumentParser: {remaining_args}'					)

										return (*outputs,)





				def   _lowerCAmelCase							(			self						,					_a						,					_a = False					):





							"""simple docstring"""
							lowerCamelCase      =   set(args.keys()					)
							lowerCamelCase      =   []
							for dtype in self.dataclass_types:
										lowerCamelCase      =   {f.name for f in dataclasses.fields(_a					) if f.init}
										lowerCamelCase      =   {k: v for k, v in args.items() if k in keys}
										unused_keys.difference_update(inputs.keys()					)
										lowerCamelCase      =   dtype(**_a					)
										outputs.append(_a					)
							if not allow_extra_keys and unused_keys:
										raise ValueError(f'Some keys are not used by the HfArgumentParser: {sorted(_a					)}'					)
							return tuple(_a					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a = False					):





							"""simple docstring"""
							with open(Path(_a					)						,					encoding="""utf-8"""					) as open_json_file:
										lowerCamelCase      =   json.loads(open_json_file.read()					)
							lowerCamelCase      =   self.parse_dict(_a						,					allow_extra_keys=_a					)
							return tuple(_a					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a = False					):





							"""simple docstring"""
							lowerCamelCase      =   self.parse_dict(yaml.safe_load(Path(_a					).read_text()					)						,					allow_extra_keys=_a					)
							return tuple(_a					)









"""simple docstring"""




from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class        __magic_name__				(			UpperCAmelCase__		,   UpperCAmelCase__		,   UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]





				@register_to_config
				def __init__(			self						,					_a						,					_a						,					_a = None						,					_a = 50_257						,					_a = 1_024						,					_a = 768						,					_a = 12						,					_a = 12						,					_a = None						,					_a = "gelu_new"						,					_a = 0.1						,					_a = 0.1						,					_a = 0.1						,					_a = 1e-5						,					_a = 0.02						,					_a = True						,					_a = True						,					_a = False						,					_a = False						,					):





							"""simple docstring"""
							super().__init__()

							lowerCamelCase      =   prefix_length

							if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
										raise ValueError(
										    f'`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'
										    f' `n_embd`: {n_embd} are not equal.'					)

							lowerCamelCase      =   prefix_inner_dim
							lowerCamelCase      =   prefix_hidden_dim

							lowerCamelCase      =   (
							    nn.Linear(self.prefix_inner_dim						,					self.prefix_hidden_dim					)
							    if self.prefix_hidden_dim is not None
							    else nn.Identity()
							)
							lowerCamelCase      =   (
							    nn.Linear(self.prefix_hidden_dim						,					_a					) if self.prefix_hidden_dim is not None else nn.Identity()
							)

							lowerCamelCase      =   GPTaConfig(
							    vocab_size=_a						,					n_positions=_a						,					n_embd=_a						,					n_layer=_a						,					n_head=_a						,					n_inner=_a						,					activation_function=_a						,					resid_pdrop=_a						,					embd_pdrop=_a						,					attn_pdrop=_a						,					layer_norm_epsilon=_a						,					initializer_range=_a						,					scale_attn_weights=_a						,					use_cache=_a						,					scale_attn_by_inverse_layer_idx=_a						,					reorder_and_upcast_attn=_a						,					)
							lowerCamelCase      =   GPTaLMHeadModel(_a					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a = None						,					_a = None						,					):





							"""simple docstring"""
							lowerCamelCase      =   self.transformer.transformer.wte(_a					)
							lowerCamelCase      =   self.encode_prefix(_a					)
							lowerCamelCase      =   self.decode_prefix(_a					)
							lowerCamelCase      =   torch.cat((prefix_embeds, embedding_text)						,					dim=1					)

							if labels is not None:
										lowerCamelCase      =   self.get_dummy_token(input_ids.shape[0]						,					input_ids.device					)
										lowerCamelCase      =   torch.cat((dummy_token, input_ids)						,					dim=1					)
							lowerCamelCase      =   self.transformer(inputs_embeds=_a						,					labels=_a						,					attention_mask=_a					)
							if self.prefix_hidden_dim is not None:
										return out, hidden
							else:
										return out





				def   _lowerCAmelCase							(			self						,					_a						,					_a					):





							"""simple docstring"""
							return torch.zeros(_a						,					self.prefix_length						,					dtype=torch.intaa						,					device=_a					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							return self.encode_prefix(_a					)





				@torch.no_grad()
				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   torch.split(_a						,					1						,					dim=0					)
							lowerCamelCase      =   []
							lowerCamelCase      =   []
							for feature in features:
										lowerCamelCase      =   self.decode_prefix(feature.to(_a					)					)  # back to the clip feature
										# Only support beam search for now
										lowerCamelCase ,  lowerCamelCase      =   self.generate_beam(
										    input_embeds=_a						,					device=_a						,					eos_token_id=_a					)
										generated_tokens.append(output_tokens[0]					)
										generated_seq_lengths.append(seq_lengths[0]					)
							lowerCamelCase      =   torch.stack(_a					)
							lowerCamelCase      =   torch.stack(_a					)
							return generated_tokens, generated_seq_lengths





				@torch.no_grad()
				def   _lowerCAmelCase							(			self						,					_a=None						,					_a=None						,					_a=None						,					_a = 5						,					_a = 67						,					_a = 1.0						,					_a = None						,					):





							"""simple docstring"""
							lowerCamelCase      =   eos_token_id
							lowerCamelCase      =   None
							lowerCamelCase      =   None
							lowerCamelCase      =   torch.ones(_a						,					device=_a						,					dtype=torch.int					)
							lowerCamelCase      =   torch.zeros(_a						,					device=_a						,					dtype=torch.bool					)

							if input_embeds is not None:
										lowerCamelCase      =   input_embeds
							else:
										lowerCamelCase      =   self.transformer.transformer.wte(_a					)

							for i in range(_a					):
										lowerCamelCase      =   self.transformer(inputs_embeds=_a					)
										lowerCamelCase      =   outputs.logits
										lowerCamelCase      =   logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
										lowerCamelCase      =   logits.softmax(-1					).log()

										if scores is None:
													lowerCamelCase ,  lowerCamelCase      =   logits.topk(_a						,					-1					)
													lowerCamelCase      =   generated.expand(_a						,					*generated.shape[1:]					)
													lowerCamelCase ,  lowerCamelCase      =   next_tokens.permute(1						,					0					), scores.squeeze(0					)
													if tokens is None:
																lowerCamelCase      =   next_tokens
													else:
																lowerCamelCase      =   tokens.expand(_a						,					*tokens.shape[1:]					)
																lowerCamelCase      =   torch.cat((tokens, next_tokens)						,					dim=1					)
										else:
													lowerCamelCase      =   -float(np.inf					)
													lowerCamelCase      =   0
													lowerCamelCase      =   scores[:, None] + logits
													seq_lengths[~is_stopped] += 1
													lowerCamelCase      =   scores_sum / seq_lengths[:, None]
													lowerCamelCase ,  lowerCamelCase      =   scores_sum_average.view(-1					).topk(_a						,					-1					)
													lowerCamelCase      =   next_tokens // scores_sum.shape[1]
													lowerCamelCase      =   seq_lengths[next_tokens_source]
													lowerCamelCase      =   next_tokens % scores_sum.shape[1]
													lowerCamelCase      =   next_tokens.unsqueeze(1					)
													lowerCamelCase      =   tokens[next_tokens_source]
													lowerCamelCase      =   torch.cat((tokens, next_tokens)						,					dim=1					)
													lowerCamelCase      =   generated[next_tokens_source]
													lowerCamelCase      =   scores_sum_average * seq_lengths
													lowerCamelCase      =   is_stopped[next_tokens_source]

										lowerCamelCase      =   self.transformer.transformer.wte(next_tokens.squeeze()					).view(generated.shape[0]						,					1						,					-1					)
										lowerCamelCase      =   torch.cat((generated, next_token_embed)						,					dim=1					)
										lowerCamelCase      =   is_stopped + next_tokens.eq(_a					).squeeze()
										if is_stopped.all():
													break

							lowerCamelCase      =   scores / seq_lengths
							lowerCamelCase      =   scores.argsort(descending=_a					)
							# tokens tensors are already padded to max_seq_length
							lowerCamelCase      =   [tokens[i] for i in order]
							lowerCamelCase      =   torch.stack(_a						,					dim=0					)
							lowerCamelCase      =   torch.tensor([seq_lengths[i] for i in order]						,					dtype=seq_lengths.dtype					)
							return output_texts, seq_lengths



